Nuclear Medicine

Leveraging Large Language Models to Direct Automated PET/CT Tumour Segmentation in Retrospective Data: an Agentic Framework Method

Automated medical image segmentation using deep learning requires large labelled datasets, presenting barriers for rare cancers like sarcoma. We developed an agentic framework integrating LLM analysis of radiologist reports with nnUNet segmentation for 18F-FDG PET/CT imaging data (N=60, 134 studies), aiming to improve automated tumour segmentation for retrospective data. LLM interpretation was opt...

Brain Region-Centered MultiModal Hypergraph Fusion for MCI Conversion Prediction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conversion prediction is critical for early intervention and resource allocation. Recently, deep learning–based multi-modal neuroimaging fusion has become a hot research topic in AI-assisted AD diagnosis. Existing multimodal fusion approaches are limited b...

Personalized prediction of breast cancer candidates for Anti-HER2 therapy using F-FDG PET/CT parameters and machine learning: a dual-center study.

BACKGROUND: Accurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop indiv...

Virtual Monochromatic Imaging of Half-Iodine-Load, Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction in Patients with Renal Insufficiency: A Clinical Pilot Study.

BACKGROUND: We retrospectively examined image quality (IQ) of thin-slice virtual monochromatic imaging (VMI) of half-iodine-load, abdominopelvic, cont...

Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks

Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective tre...

Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction

Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned ...

Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction

Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomograp...

Generative-Model-Based Fully 3D PET Image Reconstruction by Conditional Diffusion Sampling

Score-based generative models (SGMs) have recently shown promising results for image reconstruction on simulated positron emission tomography (PET) ...

End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms

As a sensitive functional imaging technique, positron emission tomography (PET) plays a critical role in early disease diagnosis. However, obtaining...

Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques s...

Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Di...

Cerebral microbleeds: Association with cognitive decline and pathology build-up

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alz...

Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.

OBJECTIVE: If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the rec...

PET Myocardial Flow Reserve Estimation from 4D-Coronary-CT using Deep Neural Network.

The myocardial flow reserve (MFR) index proves to be a highly effective means of assessing the severity of myocardial ischemic disease. An MFR value b...

Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction.

Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measure...

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typica...

AI chatbots in pet health care: Opportunities and challenges for owners.

The integration of artificial intelligence (AI) into health care has seen remarkable advancements, with applications extending to animal health. This ...

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medi...

Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques.

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and sur...

[[PET] 9. Current Status of the Artificial Intelligence Image Reconstruction on Clinical PET Equipment].